------------------------------------------------------------------------------------------------------------------------------------
      name:  plog_921
       log:  /accounts/projects/jr_ra/GRscarring/erratum/programs/analysis/miscstats.log
  log type:  text
 opened on:  27 Nov 2024, 18:06:02

. clear

. cap project, doinfo

. //cap notaproject
. if _rc==0 {
.         local pdir "`r(pdir)'"                                                      // the project's main dir.
.         local dofile "`r(dofile)'"                                                  // do-file's stub name
.         local sig {bind:{hi:[`dofile'.dta. RP : `dofile'.do, `c(current_date)']}}       // a signature in notes
.         local doasproject=1
. }

. else {
.         local pdir "~/GRscarring"
.         local dofile "miscstats"
.         local doasproject=0
. }

. 
. set more off

. local rootdir "`pdir'"

. local thisdir "`pdir'"

. 
. local scratch "`pdir'/scratch"

. local rawdata "`pdir'/rawdata"

. local output "`pdir'/results"

. 
. set scheme s1color

. 
. if `doasproject'==1 {
.         project, uses(`scratch'/extrapolate_coeffs.dta)
project GRscar_erratum > do-file uses: "/scratch/public/jr_ra/GRscarring2024/erratum/scratch/extrapolate_coeffs.dta" filesig(3506803
> 25:2415967)
.         project, uses(`scratch'/runatc_coeffs.dta)
project GRscar_erratum > do-file uses: "/scratch/public/jr_ra/GRscarring2024/erratum/scratch/runatc_coeffs.dta" filesig(1418047973:1
> 958395)
.         project, uses(`scratch'/cohfxregs.dta)
project GRscar_erratum > do-file uses: "/scratch/public/jr_ra/GRscarring2024/erratum/scratch/cohfxregs.dta" filesig(3130744956:21418
> 09)
.         project, uses(`scratch'/fig_ur.dta)
project GRscar_erratum > do-file uses: "/scratch/public/jr_ra/GRscarring2024/erratum/scratch/fig_ur.dta" filesig(3758749835:63094)
.         project, uses(`scratch'/fig_ur_age.dta)
project GRscar_erratum > do-file uses: "/scratch/public/jr_ra/GRscarring2024/erratum/scratch/fig_ur_age.dta" filesig(3306211396:4476
> 44)
.         project, uses(`scratch'/extractcps.dta.gz)
project GRscar_erratum > do-file uses: "/scratch/public/jr_ra/GRscarring2024/erratum/scratch/extractcps.dta.gz" filesig(1117482818:1
> 425161591)
.         project, uses(`scratch'/extractorg_morg.dta.gz)
project GRscar_erratum > do-file uses: "/scratch/public/jr_ra/GRscarring2024/erratum/scratch/extractorg_morg.dta.gz" filesig(8195742
> 91:643171877)
. }

. 
. // The cohort that entered the labor market in 2010 has had an employment rate that, 
. // averaged over its experience to date, is ## percentage points lower than what would
. // have been expected based on prior cohorts’ age profiles and the state of the economy.
. use `scratch'/extrapolate_coeffs

. keep if depvar=="empl"
(14,359 observations deleted)

. keep if ivartype=="FV" & fvname=="entrycohort" & model=="mB1b"
(7,209 observations deleted)

. gen educ=real(substr(model,3,1))

. keep if educ==1
(0 observations deleted)

. replace b = b*100 if depvar=="empl"
(48 real changes made)

. rename fvval entrycohort

. reg b entrycohort if entrycohort<2005

      Source |       SS           df       MS      Number of obs   =        35
-------------+----------------------------------   F(1, 33)        =      9.18
       Model |  .748279987         1  .748279987   Prob > F        =    0.0047
    Residual |  2.69068689        33  .081535966   R-squared       =    0.2176
-------------+----------------------------------   Adj R-squared   =    0.1939
       Total |  3.43896687        34  .101146084   Root MSE        =    .28555

------------------------------------------------------------------------------
           b | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 entrycohort |  -.0144776    .004779    -3.03   0.005    -.0242007   -.0047546
       _cons |   28.67832   9.496072     3.02   0.005     9.358419    47.99823
------------------------------------------------------------------------------

. predict fvvalhat, xb

. gen resid=b-fvvalhat

. list entrycohort b fvvalhat resid

     +------------------------------------------------+
     | entryc~t           b     fvvalhat        resid |
     |------------------------------------------------|
  1. |     1970    1.021694    .15735955    .86433438 |
  2. |     1971    .2171233     .1428819    .07424138 |
  3. |     1972    .3958225    .12840426    .26741822 |
  4. |     1973    .5108188    .11392661    .39689223 |
  5. |     1974    .0327424    .09944897   -.06670661 |
     |------------------------------------------------|
  6. |     1975   -.2209482    .08497132   -.30591947 |
  7. |     1976   -.3791822    .07049367   -.44967591 |
  8. |     1977   -.1958854    .05601603   -.25190141 |
  9. |     1978     .093339    .04153838    .05180062 |
 10. |     1979    .2211398    .02706074    .19407906 |
     |------------------------------------------------|
 11. |     1980   -.2156493    .01258309   -.22823241 |
 12. |     1981   -.1225696   -.00189456   -.12067505 |
 13. |     1982   -.4692452    -.0163722   -.45287305 |
 14. |     1983   -.1792495   -.03084985   -.14839965 |
 15. |     1984           0    -.0453275     .0453275 |
     |------------------------------------------------|
 16. |     1985   -.2952679   -.05980514   -.23546274 |
 17. |     1986   -.4052011   -.07428279    -.3309183 |
 18. |     1987    .0193326   -.08876043    .10809301 |
 19. |     1988   -.0692706   -.10323808    .03396745 |
 20. |     1989   -.0816617   -.11771573    .03605404 |
     |------------------------------------------------|
 21. |     1990   -.3928682   -.13219337   -.26067481 |
 22. |     1991    -.517925   -.14667102     -.371254 |
 23. |     1992    -.342934   -.16114866   -.18178529 |
 24. |     1993   -.0183219   -.17562631    .15730438 |
 25. |     1994   -.3148885   -.19010396   -.12478452 |
     |------------------------------------------------|
 26. |     1995    .0016276    -.2045816    .20620924 |
 27. |     1996   -.0403765   -.21905925     .1786828 |
 28. |     1997    .0436181    -.2335369    .27715499 |
 29. |     1998   -.0436257   -.24801454    .20438883 |
 30. |     1999     .209773   -.26249219    .47226522 |
     |------------------------------------------------|
 31. |     2000           0   -.27696983    .27696983 |
 32. |     2001   -.3645651   -.29144748   -.07311758 |
 33. |     2002    -.451823   -.30592513   -.14589783 |
 34. |     2003   -.2529916   -.32040277    .06741116 |
 35. |     2004   -.4991961   -.33488042    -.1643157 |
     |------------------------------------------------|
 36. |     2005   -1.075808   -.34935806   -.72644952 |
 37. |     2006   -.9419939   -.36383571   -.57815814 |
 38. |     2007   -1.162088   -.37831336   -.78377493 |
 39. |     2008   -1.554973     -.392791    -1.162182 |
 40. |     2009   -2.080788   -.40726865   -1.6735193 |
     |------------------------------------------------|
 41. |     2010   -2.597179   -.42174629   -2.1754322 |
 42. |     2011   -2.524334   -.43622394   -2.0881099 |
 43. |     2012   -2.802794   -.45070159   -2.3520928 |
 44. |     2013   -3.008173   -.46517923   -2.5429936 |
 45. |     2014   -3.399704   -.47965688   -2.9200475 |
     |------------------------------------------------|
 46. |     2015   -3.511866   -.49413453   -3.0177317 |
 47. |     2016   -3.427475   -.50861217   -2.9188628 |
 48. |     2017   -4.413418   -.52308982   -3.8903287 |
 49. |     2018   -5.046054   -.53756746   -4.5084861 |
 50. |     2019   -4.698621   -.55204511   -4.1465764 |
     +------------------------------------------------+

. 
. 
. // The most recent cohorts have employment rates three to four percentage points lower 
. // than what one would have anticipated based on the pre-2005 trend. 
. use `scratch'/cohfxregs, clear

. keep if depvar=="empl" & entrycohort<.
(8,960 observations deleted)

. list entrycohort b  ur0 fitted_preGR_1 resid_preGR_1 if model=="mB1b"

      +------------------------------------------------------+
      | entryc~t           b   ur0   fitted_p~1   resid_pr~1 |
      |------------------------------------------------------|
 586. |     1970    1.021694   4.9    .49696935    .52472457 |
 587. |     1971    .2171233   5.9    .31672255   -.09959927 |
 588. |     1972    .3958225   5.6     .3421857    .05363678 |
 589. |     1973    .5108188   4.9    .43094422    .07987462 |
 590. |     1974    .0327424   5.6    .29816895   -.26542659 |
      |------------------------------------------------------|
 591. |     1975   -.2209482   8.5   -.18273086   -.03821729 |
 592. |     1976   -.3791822   7.7    -.0781485   -.30103374 |
 593. |     1977   -.1958854   7.1   -.00521382   -.19067156 |
 594. |     1978     .093339   6.1    .13101623   -.03767723 |
 595. |     1979    .2211398   5.8    .15647938    .06466042 |
      |------------------------------------------------------|
 596. |     1980   -.2156493   7.1   -.07123895   -.14441037 |
 597. |     1981   -.1225696   7.6   -.17236654    .04979694 |
 598. |     1982   -.4692452   9.7   -.52667561    .05743036 |
 599. |     1983   -.1792495   9.6   -.53286015    .35361064 |
 600. |     1984           0   7.5   -.22256783    .22256783 |
      |------------------------------------------------------|
 601. |     1985   -.2952679   7.2   -.19710468    -.0981632 |
 602. |     1986   -.4052011     7   -.18746537   -.21773571 |
 603. |     1987    .0193326   6.2   -.08288301    .10221558 |
 604. |     1988   -.0692706   5.5    .00587551   -.07514614 |
 605. |     1989   -.0816617   5.3    .01551482   -.09717651 |
      |------------------------------------------------------|
 606. |     1990   -.3928682   5.6   -.05396508    -.3389031 |
 607. |     1991    -.517925   6.8   -.26585957   -.25206545 |
 608. |     1992    -.342934   7.5   -.39863484    .05570089 |
 609. |     1993   -.0183219   6.9   -.32570017    .30737823 |
 610. |     1994   -.3148885   6.1    -.2211178   -.09377067 |
      |------------------------------------------------------|
 611. |     1995    .0016276   5.6   -.16400697     .1656346 |
 612. |     1996   -.0403765   5.4   -.15436766    .11399121 |
 613. |     1997    .0436181   4.9   -.09725682    .14087492 |
 614. |     1998   -.0436257   4.5   -.05596983    .01234411 |
 615. |     1999     .209773   4.2   -.03050668    .24027971 |
      |------------------------------------------------------|
 616. |     2000           0     4   -.02086737    .02086737 |
 617. |     2001   -.3645651   4.7   -.15364264   -.21092241 |
 618. |     2002    -.451823   5.8   -.34971329   -.10210966 |
 619. |     2003   -.2529916     6   -.40336935    .15037774 |
 620. |     2004   -.4991961   5.5   -.34625851    -.1529376 |
      |------------------------------------------------------|
 621. |     2005   -1.075808   5.1   -.30497152   -.77083607 |
 622. |     2006   -.9419939   4.6   -.24786068   -.69413317 |
 623. |     2007   -1.162088   4.6   -.26986906   -.89221923 |
 624. |     2008   -1.554973   5.8   -.48176355   -1.0732095 |
 625. |     2009   -2.080788   9.3   -1.0576064   -1.0231815 |
      |------------------------------------------------------|
 626. |     2010   -2.597179   9.6   -1.1270863   -1.4700922 |
 627. |     2011   -2.524334   8.9   -1.0383278    -1.486006 |
 628. |     2012   -2.802794   8.1   -.93374543    -1.869049 |
 629. |     2013   -3.008173   7.4   -.84498691   -2.1631859 |
 630. |     2014   -3.399704   6.2   -.67710918   -2.7225952 |
      |------------------------------------------------------|
 631. |     2015   -3.511866   5.3   -.55670297   -2.9551632 |
 632. |     2016   -3.427475   4.9   -.51541598    -2.912059 |
 633. |     2017   -4.413418   4.4   -.45830514   -3.9551133 |
 634. |     2018   -5.046054   3.9   -.40119431   -4.6448593 |
 635. |     2019   -4.698621   3.7     -.391555   -4.3070665 |
      +------------------------------------------------------+

. 
. 
. // The headline unemployment rate rose by 5.6 percentage points between mid-2007 and late 
. // 2009, while the prime-age (25-54) non-employment rate rose by over 5 percentage points 
. // (Figure 2). 
. use `scratch'/fig_ur, clear

. list yearmo year mon ur_nat_s nonemploy_r nonemploy_r_sa_m7 if year>=2007 & year<=2009

     +---------------------------------------------------------+
     |  yearmo   year   mon   ur_nat_s   nonempl~r   nonempl~7 |
     |---------------------------------------------------------|
217. |  2007m1   2007     1        4.6    .2021932   19.859762 |
218. |  2007m2   2007     2        4.5   .20411025   19.884551 |
219. |  2007m3   2007     3        4.4   .20072083   19.915694 |
220. |  2007m4   2007     4        4.5   .19971515   19.978276 |
221. |  2007m5   2007     5        4.4   .19792404   20.035816 |
     |---------------------------------------------------------|
222. |  2007m6   2007     6        4.6   .20172412   20.079745 |
223. |  2007m7   2007     7        4.7   .20525415   20.136134 |
224. |  2007m8   2007     8        4.6   .20249931    20.17371 |
225. |  2007m9   2007     9        4.7   .19905125   20.229605 |
226. | 2007m10   2007    10        4.7   .19822489   20.252366 |
     |---------------------------------------------------------|
227. | 2007m11   2007    11        4.7   .19761496    20.24193 |
228. | 2007m12   2007    12          5   .20122876   20.235162 |
229. |  2008m1   2008     1          5   .20578562   20.211044 |
230. |  2008m2   2008     2        4.9   .20676957   20.246956 |
231. |  2008m3   2008     3        5.1   .20552503   20.321212 |
     |---------------------------------------------------------|
232. |  2008m4   2008     4          5   .20294982   20.414598 |
233. |  2008m5   2008     5        5.4   .20364227   20.544969 |
234. |  2008m6   2008     6        5.6   .20720046   20.673643 |
235. |  2008m7   2008     7        5.8   .21069594   20.832761 |
236. |  2008m8   2008     8        6.1    .2118066   21.037586 |
     |---------------------------------------------------------|
237. |  2008m9   2008     9        6.1   .20880138    21.30298 |
238. | 2008m10   2008    10        6.5   .20939568   21.634506 |
239. | 2008m11   2008    11        6.8   .21401761   22.027565 |
240. | 2008m12   2008    12        7.3   .22336872   22.474019 |
241. |  2009m1   2009     1        7.8   .23545014   22.896797 |
     |---------------------------------------------------------|
242. |  2009m2   2009     2        8.3   .23879167   23.293405 |
243. |  2009m3   2009     3        8.7   .24084478   23.615293 |
244. |  2009m4   2009     4          9   .23691045   23.829636 |
245. |  2009m5   2009     5        9.4   .23942916   23.999574 |
246. |  2009m6   2009     6        9.5   .24198356   24.134813 |
     |---------------------------------------------------------|
247. |  2009m7   2009     7        9.5   .24466476   24.281446 |
248. |  2009m8   2009     8        9.6   .24456546   24.455129 |
249. |  2009m9   2009     9        9.8   .24543031   24.668564 |
250. | 2009m10   2009    10         10   .24389195   24.830786 |
251. | 2009m11   2009    11        9.9   .24365019   24.937535 |
     |---------------------------------------------------------|
252. | 2009m12   2009    12        9.9   .25159518   25.010426 |
     +---------------------------------------------------------+

. su  ur_nat_s  if year>=2007 & year<=2009

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    ur_nat_s |         36    6.566667    2.094755        4.4         10

. di r(max)-r(min)
5.6

. su  nonemploy_r  if year>=2007 & year<=2009

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 nonemploy_r |         36    .2174285    .0186192    .197615   .2515952

. di r(max)-r(min)
.05398022

. 
. // Unemployment began to recover in mid 2010 and declined roughly linearly, at a rate of 
. // about 0.9 percentage points per year, thereafter.
. list yearmo year mon ur_nat_s nonemploy_r nonemploy_r_sa_m7 if year>=2010 

     +---------------------------------------------------------+
     |  yearmo   year   mon   ur_nat_s   nonempl~r   nonempl~7 |
     |---------------------------------------------------------|
253. |  2010m1   2010     1        9.8    .2541459   24.960964 |
254. |  2010m2   2010     2        9.8   .25428013    24.92585 |
255. |  2010m3   2010     3        9.9   .25218074   24.878815 |
256. |  2010m4   2010     4        9.9   .24481616   24.811765 |
257. |  2010m5   2010     5        9.6   .24667432   24.810682 |
     |---------------------------------------------------------|
258. |  2010m6   2010     6        9.4   .24851237   24.803835 |
259. |  2010m7   2010     7        9.4   .25170883     24.8251 |
260. |  2010m8   2010     8        9.5      .24981   24.881681 |
261. |  2010m9   2010     9        9.5   .24585443   24.946982 |
262. | 2010m10   2010    10        9.4   .24413596   24.999524 |
     |---------------------------------------------------------|
263. | 2010m11   2010    11        9.8   .24772513   25.030128 |
264. | 2010m12   2010    12        9.3   .24921302   25.007783 |
265. |  2011m1   2011     1        9.1     .253902    24.94475 |
266. |  2011m2   2011     2          9   .25360477   24.895638 |
267. |  2011m3   2011     3          9   .25023192   24.859867 |
     |---------------------------------------------------------|
268. |  2011m4   2011     4        9.1    .2478737   24.855436 |
269. |  2011m5   2011     5          9   .24569435   24.863152 |
270. |  2011m6   2011     6        9.1   .25095939   24.883425 |
271. |  2011m7   2011     7          9   .25195306   24.905667 |
272. |  2011m8   2011     8          9   .24969594   24.923317 |
     |---------------------------------------------------------|
273. |  2011m9   2011     9          9   .24706986   24.948295 |
274. | 2011m10   2011    10        8.8   .24552246   24.915169 |
275. | 2011m11   2011    11        8.6   .24377693   24.830992 |
276. | 2011m12   2011    12        8.5   .24570511   24.714884 |
277. |  2012m1   2012     1        8.3   .25005717   24.561699 |
     |---------------------------------------------------------|
278. |  2012m2   2012     2        8.3    .2492728   24.443204 |
279. |  2012m3   2012     3        8.2    .2454543   24.358803 |
280. |  2012m4   2012     4        8.2    .2415497   24.304084 |
281. |  2012m5   2012     5        8.2   .24011214   24.279641 |
282. |  2012m6   2012     6        8.2   .24422941   24.250224 |
     |---------------------------------------------------------|
283. |  2012m7   2012     7        8.2   .24608094    24.21331 |
284. |  2012m8   2012     8        8.1   .24345639   24.187114 |
285. |  2012m9   2012     9        7.8   .23694533   24.176672 |
286. | 2012m10   2012    10        7.8   .23487982   24.191036 |
287. | 2012m11   2012    11        7.7   .23926014    24.22559 |
     |---------------------------------------------------------|
288. | 2012m12   2012    12        7.9    .2408979   24.235674 |
289. |  2013m1   2013     1          8   .24865603   24.216482 |
290. |  2013m2   2013     2        7.7   .24611608   24.163436 |
291. |  2013m3   2013     3        7.5   .24348664   24.099138 |
292. |  2013m4   2013     4        7.6   .23942291   24.037789 |
     |---------------------------------------------------------|
293. |  2013m5   2013     5        7.5   .23646733    23.99083 |
294. |  2013m6   2013     6        7.5   .24130334    23.97566 |
295. |  2013m7   2013     7        7.3   .24238497   24.003515 |
296. |  2013m8   2013     8        7.2   .24146843   24.062334 |
297. |  2013m9   2013     9        7.2   .23693877   24.136354 |
     |---------------------------------------------------------|
298. | 2013m10   2013    10        7.2   .24026815   24.162384 |
299. | 2013m11   2013    11        6.9   .23707496   24.080676 |
300. | 2013m12   2013    12        6.7   .23896047   23.929523 |
301. |  2014m1   2014     1        6.6   .24064038   23.722005 |
302. |  2014m2   2014     2        6.7   .23935025   23.555543 |
     |---------------------------------------------------------|
303. |  2014m3   2014     3        6.7    .2356161   23.439833 |
304. |  2014m4   2014     4        6.2   .23320204   23.372377 |
305. |  2014m5   2014     5        6.3   .23242804   23.331282 |
306. |  2014m6   2014     6        6.1    .2323041   23.269592 |
307. |  2014m7   2014     7        6.2   .23607776   23.240444 |
     |---------------------------------------------------------|
308. |  2014m8   2014     8        6.1   .23287944   23.215385 |
309. |  2014m9   2014     9        5.9   .22849469   23.215028 |
310. | 2014m10   2014    10        5.7   .22735785   23.200568 |
311. | 2014m11   2014    11        5.8   .22803138   23.132477 |
312. | 2014m12   2014    12        5.6   .22952332   23.029662 |
     |---------------------------------------------------------|
313. |  2015m1   2015     1        5.7   .23321352   22.888867 |
314. |  2015m2   2015     2        5.5   .23118597   22.779654 |
315. |  2015m3   2015     3        5.4   .22939165   22.713893 |
316. |  2015m4   2015     4        5.4   .22585806   22.691915 |
317. |  2015m5   2015     5        5.6   .22450275   22.705771 |
     |---------------------------------------------------------|
318. |  2015m6   2015     6        5.3   .22771767   22.725223 |
319. |  2015m7   2015     7        5.2   .23195733   22.767401 |
320. |  2015m8   2015     8        5.1   .22914424   22.796704 |
321. |  2015m9   2015     9          5   .22426125    22.82826 |
322. | 2015m10   2015    10          5   .22477904   22.810046 |
     |---------------------------------------------------------|
323. | 2015m11   2015    11        5.1   .22330054   22.700063 |
324. | 2015m12   2015    12          5   .22580519   22.535755 |
325. |  2016m1   2016     1        4.9   .22696506   22.322993 |
326. |  2016m2   2016     2        4.9   .22451293   22.155845 |
327. |  2016m3   2016     3          5   .22091399   22.066338 |
     |---------------------------------------------------------|
328. |  2016m4   2016     4          5   .22048125    22.04629 |
329. |  2016m5   2016     5        4.8   .21848457   22.073041 |
330. |  2016m6   2016     6        4.9   .22242056   22.096216 |
331. |  2016m7   2016     7        4.8    .2238409   22.107785 |
332. |  2016m8   2016     8        4.9   .22386304    22.10921 |
     |---------------------------------------------------------|
333. |  2016m9   2016     9          5   .21700617   22.100844 |
334. | 2016m10   2016    10        4.9   .21536448   22.066146 |
335. | 2016m11   2016    11        4.7   .21598241   21.990135 |
336. | 2016m12   2016    12        4.7   .21869244   21.867497 |
337. |  2017m1   2017     1        4.7   .22222334   21.700434 |
     |---------------------------------------------------------|
338. |  2017m2   2017     2        4.6   .21905373   21.559171 |
339. |  2017m3   2017     3        4.4   .21562586   21.463621 |
340. |  2017m4   2017     4        4.4    .2121817   21.414564 |
341. |  2017m5   2017     5        4.4   .21308576   21.438462 |
342. |  2017m6   2017     6        4.3   .21580848    21.43842 |
     |---------------------------------------------------------|
343. |  2017m7   2017     7        4.3   .21690793   21.433153 |
344. |  2017m8   2017     8        4.4   .21856204   21.413851 |
345. |  2017m9   2017     9        4.2   .20769168    21.33932 |
346. | 2017m10   2017    10        4.1   .20862068   21.274093 |
347. | 2017m11   2017    11        4.2   .20678183   21.148545 |
     |---------------------------------------------------------|
348. | 2017m12   2017    12        4.1   .20912006   20.999555 |
349. |  2018m1   2018     1        4.1   .21469503   20.857509 |
350. |  2018m2   2018     2        4.1   .20913674   20.725469 |
351. |  2018m3   2018     3          4   .20786745   20.674979 |
352. |  2018m4   2018     4          4   .20655679   20.659559 |
     |---------------------------------------------------------|
353. |  2018m5   2018     5        3.8   .20481678   20.672027 |
354. |  2018m6   2018     6          4   .20845205   20.689863 |
355. |  2018m7   2018     7        3.8   .20876413    20.67558 |
356. |  2018m8   2018     8        3.8    .2096738   20.658104 |
357. |  2018m9   2018     9        3.7    .2033713   20.624431 |
     |---------------------------------------------------------|
358. | 2018m10   2018    10        3.8   .19952187   20.544707 |
359. | 2018m11   2018    11        3.7   .19998732   20.434246 |
360. | 2018m12   2018    12        3.9   .20321357   20.297242 |
361. |  2019m1   2019     1          4   .20546633    20.14993 |
362. |  2019m2   2019     2        3.8   .20302922   20.075792 |
     |---------------------------------------------------------|
363. |  2019m3   2019     3        3.8     .201926   20.078513 |
364. |  2019m4   2019     4        3.6   .20087171   20.149388 |
365. |  2019m5   2019     5        3.6   .20078669   20.241581 |
366. |  2019m6   2019     6        3.7    .2045851   20.280608 |
367. |  2019m7   2019     7        3.7   .20896937   20.257323 |
     |---------------------------------------------------------|
368. |  2019m8   2019     8        3.7   .20315881   20.145546 |
369. |  2019m9   2019     9        3.5   .19517441   20.011218 |
370. | 2019m10   2019    10        3.6   .19306461           . |
371. | 2019m11   2019    11        3.5   .19269147           . |
372. | 2019m12   2019    12        3.5   .19485204   19.766289 |
     +---------------------------------------------------------+

. reg ur_nat_s yearmo if yearmo>=ym(2010,7) & yearmo<=ym(2015,12)

      Source |       SS           df       MS      Number of obs   =        66
-------------+----------------------------------   F(1, 64)        =   4051.79
       Model |   134.88473         1   134.88473   Prob > F        =    0.0000
    Residual |  2.13057273        64  .033290199   R-squared       =    0.9845
-------------+----------------------------------   Adj R-squared   =    0.9842
       Total |  137.015303        65  2.10792774   Root MSE        =    .18246

------------------------------------------------------------------------------
    ur_nat_s | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      yearmo |  -.0750423   .0011789   -63.65   0.000    -.0773974   -.0726871
       _cons |    55.2857   .7530727    73.41   0.000     53.78127    56.79014
------------------------------------------------------------------------------

. di _b[yearmo]*12
-.90050725

. 
. // The unemployment rate was below 6% from the third quarter of 2014 and below its
. // pre-recession level from late 2017. 
. list yearmo year mon ur_nat_s nonemploy_r nonemploy_r_sa_m7 if year>=2010 & ur_nat_s<=6 & ur_nat_s>5 

     +---------------------------------------------------------+
     |  yearmo   year   mon   ur_nat_s   nonempl~r   nonempl~7 |
     |---------------------------------------------------------|
309. |  2014m9   2014     9        5.9   .22849469   23.215028 |
310. | 2014m10   2014    10        5.7   .22735785   23.200568 |
311. | 2014m11   2014    11        5.8   .22803138   23.132477 |
312. | 2014m12   2014    12        5.6   .22952332   23.029662 |
313. |  2015m1   2015     1        5.7   .23321352   22.888867 |
     |---------------------------------------------------------|
314. |  2015m2   2015     2        5.5   .23118597   22.779654 |
315. |  2015m3   2015     3        5.4   .22939165   22.713893 |
316. |  2015m4   2015     4        5.4   .22585806   22.691915 |
317. |  2015m5   2015     5        5.6   .22450275   22.705771 |
318. |  2015m6   2015     6        5.3   .22771767   22.725223 |
     |---------------------------------------------------------|
319. |  2015m7   2015     7        5.2   .23195733   22.767401 |
320. |  2015m8   2015     8        5.1   .22914424   22.796704 |
323. | 2015m11   2015    11        5.1   .22330054   22.700063 |
     +---------------------------------------------------------+

. list yearmo year mon ur_nat_s nonemploy_r nonemploy_r_sa_m7 if year>=2010 & ur_nat_s<=4.1

     +---------------------------------------------------------+
     |  yearmo   year   mon   ur_nat_s   nonempl~r   nonempl~7 |
     |---------------------------------------------------------|
346. | 2017m10   2017    10        4.1   .20862068   21.274093 |
348. | 2017m12   2017    12        4.1   .20912006   20.999555 |
349. |  2018m1   2018     1        4.1   .21469503   20.857509 |
350. |  2018m2   2018     2        4.1   .20913674   20.725469 |
351. |  2018m3   2018     3          4   .20786745   20.674979 |
     |---------------------------------------------------------|
352. |  2018m4   2018     4          4   .20655679   20.659559 |
353. |  2018m5   2018     5        3.8   .20481678   20.672027 |
354. |  2018m6   2018     6          4   .20845205   20.689863 |
355. |  2018m7   2018     7        3.8   .20876413    20.67558 |
356. |  2018m8   2018     8        3.8    .2096738   20.658104 |
     |---------------------------------------------------------|
357. |  2018m9   2018     9        3.7    .2033713   20.624431 |
358. | 2018m10   2018    10        3.8   .19952187   20.544707 |
359. | 2018m11   2018    11        3.7   .19998732   20.434246 |
360. | 2018m12   2018    12        3.9   .20321357   20.297242 |
361. |  2019m1   2019     1          4   .20546633    20.14993 |
     |---------------------------------------------------------|
362. |  2019m2   2019     2        3.8   .20302922   20.075792 |
363. |  2019m3   2019     3        3.8     .201926   20.078513 |
364. |  2019m4   2019     4        3.6   .20087171   20.149388 |
365. |  2019m5   2019     5        3.6   .20078669   20.241581 |
366. |  2019m6   2019     6        3.7    .2045851   20.280608 |
     |---------------------------------------------------------|
367. |  2019m7   2019     7        3.7   .20896937   20.257323 |
368. |  2019m8   2019     8        3.7   .20315881   20.145546 |
369. |  2019m9   2019     9        3.5   .19517441   20.011218 |
370. | 2019m10   2019    10        3.6   .19306461           . |
371. | 2019m11   2019    11        3.5   .19269147           . |
     |---------------------------------------------------------|
372. | 2019m12   2019    12        3.5   .19485204   19.766289 |
     +---------------------------------------------------------+

. su ur_nat_s if yearmo>=ym(2014,12)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    ur_nat_s |         61    4.429508    .6390994        3.5        5.7

. su ur_nat_s if yearmo>=ym(2017,12)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    ur_nat_s |         25       3.792    .1913113        3.5        4.1

. 
. // Only half the decline in prime-age employment had been erased by the end of 2015; the
. // employment rate did not recover its level prior to the recession until late 2019. 
. su nonemploy_r if year>=2007 & year<=2010

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 nonemploy_r |         48    .2253434     .021281    .197615   .2542801

. list yearmo year mon ur_nat_s nonemploy_r nonemploy_r_sa_m7 if inlist(year, 2015, 2016)

     +---------------------------------------------------------+
     |  yearmo   year   mon   ur_nat_s   nonempl~r   nonempl~7 |
     |---------------------------------------------------------|
313. |  2015m1   2015     1        5.7   .23321352   22.888867 |
314. |  2015m2   2015     2        5.5   .23118597   22.779654 |
315. |  2015m3   2015     3        5.4   .22939165   22.713893 |
316. |  2015m4   2015     4        5.4   .22585806   22.691915 |
317. |  2015m5   2015     5        5.6   .22450275   22.705771 |
     |---------------------------------------------------------|
318. |  2015m6   2015     6        5.3   .22771767   22.725223 |
319. |  2015m7   2015     7        5.2   .23195733   22.767401 |
320. |  2015m8   2015     8        5.1   .22914424   22.796704 |
321. |  2015m9   2015     9          5   .22426125    22.82826 |
322. | 2015m10   2015    10          5   .22477904   22.810046 |
     |---------------------------------------------------------|
323. | 2015m11   2015    11        5.1   .22330054   22.700063 |
324. | 2015m12   2015    12          5   .22580519   22.535755 |
325. |  2016m1   2016     1        4.9   .22696506   22.322993 |
326. |  2016m2   2016     2        4.9   .22451293   22.155845 |
327. |  2016m3   2016     3          5   .22091399   22.066338 |
     |---------------------------------------------------------|
328. |  2016m4   2016     4          5   .22048125    22.04629 |
329. |  2016m5   2016     5        4.8   .21848457   22.073041 |
330. |  2016m6   2016     6        4.9   .22242056   22.096216 |
331. |  2016m7   2016     7        4.8    .2238409   22.107785 |
332. |  2016m8   2016     8        4.9   .22386304    22.10921 |
     |---------------------------------------------------------|
333. |  2016m9   2016     9          5   .21700617   22.100844 |
334. | 2016m10   2016    10        4.9   .21536448   22.066146 |
335. | 2016m11   2016    11        4.7   .21598241   21.990135 |
336. | 2016m12   2016    12        4.7   .21869244   21.867497 |
     +---------------------------------------------------------+

. list yearmo year mon ur_nat_s nonemploy_r nonemploy_r_sa_m7 if year>=2010 & nonemploy_r<0.207

     +---------------------------------------------------------+
     |  yearmo   year   mon   ur_nat_s   nonempl~r   nonempl~7 |
     |---------------------------------------------------------|
347. | 2017m11   2017    11        4.2   .20678183   21.148545 |
352. |  2018m4   2018     4          4   .20655679   20.659559 |
353. |  2018m5   2018     5        3.8   .20481678   20.672027 |
357. |  2018m9   2018     9        3.7    .2033713   20.624431 |
358. | 2018m10   2018    10        3.8   .19952187   20.544707 |
     |---------------------------------------------------------|
359. | 2018m11   2018    11        3.7   .19998732   20.434246 |
360. | 2018m12   2018    12        3.9   .20321357   20.297242 |
361. |  2019m1   2019     1          4   .20546633    20.14993 |
362. |  2019m2   2019     2        3.8   .20302922   20.075792 |
363. |  2019m3   2019     3        3.8     .201926   20.078513 |
     |---------------------------------------------------------|
364. |  2019m4   2019     4        3.6   .20087171   20.149388 |
365. |  2019m5   2019     5        3.6   .20078669   20.241581 |
366. |  2019m6   2019     6        3.7    .2045851   20.280608 |
368. |  2019m8   2019     8        3.7   .20315881   20.145546 |
369. |  2019m9   2019     9        3.5   .19517441   20.011218 |
     |---------------------------------------------------------|
370. | 2019m10   2019    10        3.6   .19306461           . |
371. | 2019m11   2019    11        3.5   .19269147           . |
372. | 2019m12   2019    12        3.5   .19485204   19.766289 |
     +---------------------------------------------------------+

. 
.   
. // Young people fared particularly poorly. 
. !zcat `scratch'/extractcps.dta.gz > `scratch'/extractcps.dta 


. use year empl unem age wgt_composite yearmo if year>=2005 & age>=22 & age<=65 using `scratch'/extractcps.dta

. collapse (mean) empl unem (rawsum) wgt_composite [aw=wgt_composite], by(yearmo age)

. tempfile agemeans agegp0 agegp1 agegp2 agegp3 agegp4 agegp5 agegp6

. save `agemeans'
file /tmp/St2868349.000004 saved as .dta format

. gen agegp=0 if age>=22 & age<=40
(4,500 missing values generated)

. replace agegp=5 if age>40 & age<=55
(2,700 real changes made)

. collapse (mean) empl unem [aw=wgt_composite], by(yearmo agegp)

. save `agegp0'
file /tmp/St2868349.000005 saved as .dta format

. use `agemeans'

. gen agegp=1 if age>=22 & age<=30
(6,300 missing values generated)

. replace agegp=2 if age>=31 & age<=40
(1,800 real changes made)

. collapse (mean) empl unem [aw=wgt_composite], by(yearmo agegp)

. save `agegp1'
file /tmp/St2868349.000006 saved as .dta format

. use `agemeans'

. gen agegp=3 if age>=22 & age<=25
(7,200 missing values generated)

. replace agegp=4 if age>=26 & age<=30
(900 real changes made)

. collapse (mean) empl unem [aw=wgt_composite], by(yearmo agegp)

. save `agegp3'
file /tmp/St2868349.000008 saved as .dta format

. use `agemeans'

. gen agegp=6 if age>=25 & age<=54
(2,520 missing values generated)

. keep if agegp<.
(2,520 observations deleted)

. collapse (mean) empl unem [aw=wgt_composite], by(yearmo agegp)

. save `agegp6'
file /tmp/St2868349.00000b saved as .dta format

. use `agegp0'

. append using `agegp1'

. append using `agegp3'

. append using `agegp6'

. label def agegp_l 0 "22-40" 1 "22-30" 2 "31-40" 3 "22-25" 4 "26-30" 5 "40-55" 6 "25-54"

. label values agegp agegp_l

. save `scratch'/miscstats_agegpempl, replace
(file /accounts/projects/jr_ra/GRscarring/erratum/scratch/miscstats_agegpempl.dta not found)
file /accounts/projects/jr_ra/GRscarring/erratum/scratch/miscstats_agegpempl.dta saved

. !rm `scratch'/extractcps.dta 


. 
. //Over the same period, the employment rate among 26-30-year-olds fell by more than 7 percentage points.  
. use  `scratch'/miscstats_agegpempl, clear

. list if yearmo>=ym(2007,1) & yearmo<=ym(2009,12) & agegp==4

      +-----------------------------------------+
      |  yearmo   agegp        empl        unem |
      |-----------------------------------------|
1154. |  2007m1   26-30   .78495773    .0559011 |
1157. |  2007m2   26-30   .77891257   .05494571 |
1160. |  2007m3   26-30   .78693166   .04748849 |
1163. |  2007m4   26-30   .79853001   .04148961 |
1166. |  2007m5   26-30   .79608261   .04337865 |
      |-----------------------------------------|
1169. |  2007m6   26-30   .79557208   .04668461 |
1172. |  2007m7   26-30   .80054732   .04759908 |
1175. |  2007m8   26-30   .79428546   .04795378 |
1178. |  2007m9   26-30   .79560366   .05064921 |
1181. | 2007m10   26-30   .80496029   .04595156 |
      |-----------------------------------------|
1184. | 2007m11   26-30      .80433   .04611507 |
1187. | 2007m12   26-30   .79402938   .05192464 |
1190. |  2008m1   26-30   .77758045   .06671253 |
1193. |  2008m2   26-30   .78370161   .05836331 |
1196. |  2008m3   26-30   .78552085   .06399103 |
      |-----------------------------------------|
1199. |  2008m4   26-30   .79372211   .05365537 |
1202. |  2008m5   26-30   .79194471   .05197631 |
1205. |  2008m6   26-30   .78877193   .05646637 |
1208. |  2008m7   26-30   .78434507   .06054238 |
1211. |  2008m8   26-30   .78036005    .0657868 |
      |-----------------------------------------|
1214. |  2008m9   26-30   .78423354   .06140803 |
1217. | 2008m10   26-30   .77304193   .06981615 |
1220. | 2008m11   26-30   .76954582    .0724842 |
1223. | 2008m12   26-30    .7578346   .08060952 |
1226. |  2009m1   26-30   .74017897   .09921824 |
      |-----------------------------------------|
1229. |  2009m2   26-30    .7400185   .10601252 |
1232. |  2009m3   26-30   .74026694   .10518635 |
1235. |  2009m4   26-30     .744784   .09911717 |
1238. |  2009m5   26-30   .73566052   .10477663 |
1241. |  2009m6   26-30    .7423117   .10241759 |
      |-----------------------------------------|
1244. |  2009m7   26-30   .74298427   .10418565 |
1247. |  2009m8   26-30   .73292466   .10526353 |
1250. |  2009m9   26-30   .73266658   .10446129 |
1253. | 2009m10   26-30   .73666742   .10264609 |
1256. | 2009m11   26-30   .73388664   .10332345 |
      |-----------------------------------------|
1259. | 2009m12   26-30   .73468181     .100927 |
      +-----------------------------------------+

. su  empl if yearmo>=ym(2007,1) & yearmo<=ym(2009,12) & agegp==4

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        empl |         36    .7711772    .0255318   .7326666   .8049603

. di r(max)-r(min)
.0722937

. 
. //Employment of young workers was particularly slow to recover: by the end of 2014, 
. //the 25-30-year-old employment rate remained 3.8 percentage points below its pre-recession 
. //peak (as compared with 2.6 percentage points for all prime-age workers)
. su empl if agegp==4 & yearmo>=ym(2003,1) & yearmo<=ym(2007,12)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        empl |         36    .7897017    .0085006   .7711449   .8049603

. local young_pre=r(max)

. su empl if agegp==6 & yearmo>=ym(2003,1) & yearmo<=ym(2007,12)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        empl |         36    .7968728    .0045944   .7864191   .8067357

. local prime_pre=r(max)

. su empl if agegp==4 & yearmo==ym(2014,7)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        empl |          1    .7496884           .   .7496884   .7496884

. local young_post=r(mean)

. su empl if agegp==6 & yearmo==ym(2014,7)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        empl |          1    .7639222           .   .7639222   .7639222

. local prime_post=r(mean)

. di "Change for 25-30 is " `young_post'-`young_pre'
Change for 25-30 is -.0552719

. di "Change for 25-54 is " `prime_post'-`prime_pre'
Change for 25-54 is -.04281351

.  
.  
. // Average real hourly wages did not fall during the recession, due to changes in the 
. // composition of workers (Daly, Hobijn, and Wiles 2012). But they began to fall after 
. // the recession ended, with a larger decline for younger workers, then recovered in 
. // the later part of the recovery. 
. !zcat `scratch'/extractorg_morg.dta.gz > `scratch'/extractorg_morg.dta 


. use year yearmo rw_l age earnwt if year>=2005 & age>=22 & age<=65 using `scratch'/extractorg_morg.dta

. collapse (mean) rw_l (rawsum) earnwt [aw=earnwt], by(yearmo age)

. tempfile agemeans agegp0 agegp1 agegp2 agegp3 agegp4 agegp5 agegp6

. save `agemeans'
file /tmp/St2868349.00000c saved as .dta format

. gen agegp=0 if age>=22 & age<=40
(4,500 missing values generated)

. replace agegp=5 if age>40 & age<=55
(2,700 real changes made)

. keep if agegp<.
(1,800 observations deleted)

. collapse (mean) rw_l [aw=earnwt], by(yearmo agegp)

. save `agegp0'
file /tmp/St2868349.00000d saved as .dta format

. use `agemeans'

. gen agegp=1 if age>=22 & age<=30
(6,300 missing values generated)

. replace agegp=2 if age>=31 & age<=40
(1,800 real changes made)

. keep if agegp<.
(4,500 observations deleted)

. collapse (mean) rw_l [aw=earnwt], by(yearmo agegp)

. save `agegp1'
file /tmp/St2868349.00000e saved as .dta format

. use `agemeans'

. gen agegp=3 if age>=22 & age<=25
(7,200 missing values generated)

. replace agegp=4 if age>=26 & age<=30
(900 real changes made)

. keep if agegp<.
(6,300 observations deleted)

. collapse (mean) rw_l [aw=earnwt], by(yearmo agegp)

. save `agegp3'
file /tmp/St2868349.00000g saved as .dta format

. use `agemeans'

. gen agegp=6 if age>=25 & age<=54
(2,520 missing values generated)

. keep if agegp<.
(2,520 observations deleted)

. collapse (mean) rw_l [aw=earnwt], by(yearmo agegp)

. save `agegp6'
file /tmp/St2868349.00000j saved as .dta format

. use `agegp0'

. append using `agegp1'

. append using `agegp3'

. append using `agegp6'

. label def agegp_l 0 "22-40" 1 "22-30" 2 "31-40" 3 "22-25" 4 "26-30" 5 "40-55" 6 "25-54"

. label values agegp agegp_l

. save `scratch'/miscstats_agegprw_l, replace
(file /accounts/projects/jr_ra/GRscarring/erratum/scratch/miscstats_agegprw_l.dta not found)
file /accounts/projects/jr_ra/GRscarring/erratum/scratch/miscstats_agegprw_l.dta saved

. !rm `scratch'/extractorg_morg.dta 


. 
. use `scratch'/miscstats_agegprw_l, clear

. gen year=year(dofm(yearmo))

. drop if agegp==.
(0 observations deleted)

. collapse (mean) rw_l, by(agegp year)

. reshape wide rw_l, i(year) j(agegp)
(j = 0 1 2 3 4 5 6)

Data                               Long   ->   Wide
-----------------------------------------------------------------------------
Number of observations              105   ->   15          
Number of variables                   3   ->   8           
j variable (7 values)             agegp   ->   (dropped)
xij variables:
                                   rw_l   ->   rw_l0 rw_l1 ... rw_l6
-----------------------------------------------------------------------------

. list if year>=2006

     +------------------------------------------------------------------------------------------+
     | year       rw_l0       rw_l1       rw_l2       rw_l3       rw_l4       rw_l5       rw_l6 |
     |------------------------------------------------------------------------------------------|
  2. | 2006   2.7660592   2.6280187   2.8888719   2.5051076   2.7308642    2.951156   2.8846064 |
  3. | 2007   2.7706941   2.6361565    2.894155   2.5147268   2.7342844   2.9531807   2.8874646 |
  4. | 2008   2.7693258    2.636603   2.8932248   2.5101733   2.7377096   2.9531917    2.887679 |
  5. | 2009   2.7774091   2.6385185   2.9076959   2.5129915    2.738782   2.9673514   2.9002074 |
  6. | 2010   2.7655894   2.6274644   2.8975669   2.4947596   2.7339276   2.9581294   2.8910938 |
     |------------------------------------------------------------------------------------------|
  7. | 2011   2.7425787    2.600836   2.8798784   2.4607234    2.712015   2.9404756   2.8712454 |
  8. | 2012     2.73094    2.585586   2.8711896    2.437092   2.7073564   2.9397107   2.8674763 |
  9. | 2013    2.727868   2.5807201   2.8715055    2.435569   2.7024769   2.9444848   2.8684835 |
 10. | 2014   2.7300588   2.5874953   2.8681807   2.4521785   2.7026504   2.9409059   2.8651298 |
 11. | 2015   2.7583625   2.6180877   2.8938863   2.4809455   2.7325327   2.9683707   2.8908736 |
     |------------------------------------------------------------------------------------------|
 12. | 2016   2.7816243   2.6409378   2.9169622   2.5045013    2.750516   2.9873905   2.9100048 |
 13. | 2017   2.8000716   2.6674166   2.9271571   2.5343585   2.7703231   2.9917964   2.9186164 |
 14. | 2018    2.821359    2.688521    2.947353   2.5494373    2.793712   3.0017913    2.934621 |
 15. | 2019   2.8484741   2.7155553    2.972633   2.5856553   2.8102611   3.0145157   2.9524485 |
     +------------------------------------------------------------------------------------------+

. su rw_l6 if inlist(year, 2009, 2013,2014)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       rw_l6 |          3     2.87794    .0193567    2.86513   2.900207

. di "Decline in wages for prime-age workers is " r(max)-r(min)
Decline in wages for prime-age workers is .03507754

. su rw_l1 if inlist(year, 2009, 2013,2014)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       rw_l1 |          3    2.602245    .0315962    2.58072   2.638519

. di "Decline in wages for 22-30 workers is " r(max)-r(min)
Decline in wages for 22-30 workers is .05779847

. 
.  
. // Figure 3 shows non-employment and unemployment for college graduates aged 22-40. The 
. // unemployment rate rose by 150% between early 2007 and late 2009, while the non-employment 
. // rate rose from 14% in January 2007 to 18% in December 2012
. use `scratch'/fig_ur_age, clear

. list yearmo unem_sa_m7 empl_sa_m7 if yearmo>=ym(2007,1) & agegp==0 & yearmo<=ym(2011,12)

      +---------------------------------+
      |  yearmo   unem_sa~7   empl_sa~7 |
      |---------------------------------|
 337. |  2007m1   2.2255815   85.742257 |
 338. |  2007m2   2.2177411   85.607515 |
 339. |  2007m3   2.2491711   85.472486 |
 340. |  2007m4   2.3098499   85.355976 |
 341. |  2007m5   2.3704678   85.287008 |
      |---------------------------------|
 342. |  2007m6   2.4287891   85.272524 |
 343. |  2007m7   2.4664936    85.31993 |
 344. |  2007m8   2.4830545   85.427483 |
 345. |  2007m9    2.493798   85.522815 |
 346. | 2007m10   2.4934993   85.586352 |
      |---------------------------------|
 347. | 2007m11   2.4740364    85.56782 |
 348. | 2007m12   2.4378393   85.503306 |
 349. |  2008m1   2.3918152   85.489221 |
 350. |  2008m2   2.3642832   85.511698 |
 351. |  2008m3   2.3798218    85.58918 |
      |---------------------------------|
 352. |  2008m4   2.4453481   85.661365 |
 353. |  2008m5   2.5650764   85.644912 |
 354. |  2008m6   2.6907809   85.589841 |
 355. |  2008m7   2.8531935    85.49211 |
 356. |  2008m8   3.0210888   85.339323 |
      |---------------------------------|
 357. |  2008m9   3.2012918   85.141554 |
 358. | 2008m10   3.4504575   84.841579 |
 359. | 2008m11   3.6962458   84.484799 |
 360. | 2008m12   3.9658232   84.155797 |
 361. |  2009m1   4.2168601   83.930864 |
      |---------------------------------|
 362. |  2009m2   4.4486199   83.799592 |
 363. |  2009m3   4.6612855   83.748237 |
 364. |  2009m4   4.8496598   83.718032 |
 365. |  2009m5   5.0448525   83.629819 |
 366. |  2009m6   5.1851394   83.561708 |
      |---------------------------------|
 367. |  2009m7   5.2961362     83.4838 |
 368. |  2009m8   5.3740512   83.382156 |
 369. |  2009m9   5.4091461   83.262049 |
 370. | 2009m10   5.4148157   83.152701 |
 371. | 2009m11    5.383891    83.07235 |
      |---------------------------------|
 372. | 2009m12   5.3093008   83.078121 |
 373. |  2010m1   5.2105421   83.182243 |
 374. |  2010m2   5.1233524    83.24328 |
 375. |  2010m3   5.0440455   83.262831 |
 376. |  2010m4   5.0521366   83.192423 |
      |---------------------------------|
 377. |  2010m5   5.0630655   83.089278 |
 378. |  2010m6   5.0478422    83.04039 |
 379. |  2010m7   5.0602329   82.981191 |
 380. |  2010m8   4.9968069   82.929621 |
 381. |  2010m9   4.9871986   82.812289 |
      |---------------------------------|
 382. | 2010m10   5.0238915   82.646868 |
 383. | 2010m11   5.0552049   82.524031 |
 384. | 2010m12   5.0837765   82.481999 |
 385. |  2011m1   5.0256597   82.522151 |
 386. |  2011m2   4.9376926   82.624861 |
      |---------------------------------|
 387. |  2011m3   4.8161652   82.770046 |
 388. |  2011m4   4.7193988   82.899546 |
 389. |  2011m5   4.6825147    83.04233 |
 390. |  2011m6   4.6257055   83.180232 |
 391. |  2011m7   4.5907775   83.228635 |
      |---------------------------------|
 392. |  2011m8   4.5280631   83.220859 |
 393. |  2011m9   4.4404549   83.142322 |
 394. | 2011m10   4.3967046   82.976934 |
 395. | 2011m11   4.3698737   82.869512 |
 396. | 2011m12    4.357181   82.818115 |
      +---------------------------------+

. su unem_sa_m7 if yearmo>=ym(2007,1) & agegp==0 & yearmo<=ym(2009,12)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  unem_sa_m7 |         36    3.451925    1.230438   2.217741   5.414816

. di "Increase in unem rate was " r(max)/r(min)
Increase in unem rate was 2.4415905

. list yearmo empl_sa_m7 if agegp==0 & inlist(yearmo, ym(2007,1), ym(2014,12))

      +---------------------+
      |  yearmo   empl_sa~7 |
      |---------------------|
 337. |  2007m1   85.742257 |
 432. | 2014m12   83.855723 |
      +---------------------+

. //Employment recovered somewhat more quickly for young graduates than for the prime-age 
. // labor force as a whole, but nevertheless did not achieve its level on the eve of the 
. //recession until mid 2018
. list yearmo empl_sa_m7 if agegp==0 & yearmo>=ym(2014,12) & yearmo<=ym(2018,12)

      +---------------------+
      |  yearmo   empl_sa~7 |
      |---------------------|
 432. | 2014m12   83.855723 |
 433. |  2015m1   83.929215 |
 434. |  2015m2    84.03204 |
 435. |  2015m3   84.156162 |
 436. |  2015m4   84.233477 |
      |---------------------|
 437. |  2015m5   84.314979 |
 438. |  2015m6   84.294423 |
 439. |  2015m7   84.204736 |
 440. |  2015m8   84.118483 |
 441. |  2015m9   83.982458 |
      |---------------------|
 442. | 2015m10    83.92997 |
 443. | 2015m11    83.95883 |
 444. | 2015m12   84.060977 |
 445. |  2016m1   84.243648 |
 446. |  2016m2     84.3995 |
      |---------------------|
 447. |  2016m3   84.516274 |
 448. |  2016m4   84.555033 |
 449. |  2016m5   84.534101 |
 450. |  2016m6    84.54505 |
 451. |  2016m7   84.544142 |
      |---------------------|
 452. |  2016m8   84.569257 |
 453. |  2016m9   84.610764 |
 454. | 2016m10   84.626386 |
 455. | 2016m11   84.661915 |
 456. | 2016m12   84.706709 |
      |---------------------|
 457. |  2017m1   84.786458 |
 458. |  2017m2   84.836219 |
 459. |  2017m3   84.911723 |
 460. |  2017m4   84.997333 |
 461. |  2017m5   85.044964 |
      |---------------------|
 462. |  2017m6   85.171533 |
 463. |  2017m7    85.27431 |
 464. |  2017m8   85.351285 |
 465. |  2017m9   85.409838 |
 466. | 2017m10   85.334958 |
      |---------------------|
 467. | 2017m11   85.226208 |
 468. | 2017m12    85.13012 |
 469. |  2018m1   85.088774 |
 470. |  2018m2   85.168955 |
 471. |  2018m3   85.292671 |
      |---------------------|
 472. |  2018m4   85.450085 |
 473. |  2018m5   85.627262 |
 474. |  2018m6   85.763589 |
 475. |  2018m7   85.886601 |
 476. |  2018m8   85.919803 |
      |---------------------|
 477. |  2018m9   85.802786 |
 478. | 2018m10   85.671355 |
 479. | 2018m11    85.54944 |
 480. | 2018m12   85.483785 |
      +---------------------+

. 
. //Figure 4 shows the graduate employment series separately for younger and older graduates. 
. //The decline in employment was about #twice# as large for the youngest graduates as for 
. //older graduates, and was much more persistent. On the eve of the recession young 
. //graduates had similar employment rates to older graduates, as they did at the previous
. //business cycle peak, but have been persistently lower since the recession’s onset. 
. //Even in the most recent data, younger graduates’ employment rates are about three 
. //percentage points lower than those of older graduates.
. su empl_sa_m7 if yearmo>=ym(2007,1) & yearmo<=ym(2014,12) & agegp==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  empl_sa_m7 |         96    82.41946    1.468202   80.53586   85.50209

. di "Decline in emp for younger graduates is " r(max)-r(min)
Decline in emp for younger graduates is 4.9662363

. su empl_sa_m7 if yearmo>=ym(2007,1) & yearmo<=ym(2014,12) & agegp==2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  empl_sa_m7 |         96    85.03729    .6769093   83.93982   86.49216

. di "Decline in emp for older graduates is " r(max)-r(min)
Decline in emp for older graduates is 2.5523392

. collapse (mean) empl, by(year agegp)

. reshape wide empl, i(year) j(agegp)
(j = 0 1 2 3 4)

Data                               Long   ->   Wide
-----------------------------------------------------------------------------
Number of observations              205   ->   41          
Number of variables                   3   ->   6           
j variable (5 values)             agegp   ->   (dropped)
xij variables:
                                   empl   ->   empl0 empl1 ... empl4
-----------------------------------------------------------------------------

. list if year>=2007

     +------------------------------------------------------------------+
     | year       empl0       empl1       empl2       empl3       empl4 |
     |------------------------------------------------------------------|
 29. | 2007    .8546748   .85275811   .85614031   .82223133   .87160085 |
 30. | 2008   .85265835   .84342503   .85975143   .79980676   .86966453 |
 31. | 2009   .83421406    .8191448   .84597791   .78041861   .84713318 |
 32. | 2010   .82968494   .81389113   .84235961   .77685706   .83978472 |
 33. | 2011   .82954734   .81398137   .84221041   .77220161   .84142145 |
     |------------------------------------------------------------------|
 34. | 2012   .83465868   .81749468    .8487109   .77199394    .8453016 |
 35. | 2013   .83520068   .81274289   .85332535   .76314996   .84498917 |
 36. | 2014   .83902708   .82054386   .85399603   .78432937   .84530125 |
 37. | 2015    .8406818   .82179354   .85616197   .77858025    .8511447 |
 38. | 2016   .84544323    .8282621   .85922746   .78460855   .85590986 |
     |------------------------------------------------------------------|
 39. | 2017   .85151184   .83706179   .86312696   .79248694    .8615147 |
 40. | 2018     .855219   .84217188    .8655974   .79448277   .86583667 |
 41. | 2019   .85872143   .84221065   .87172695   .79709678   .86680243 |
     +------------------------------------------------------------------+

. 
. 
. //Age-adjusted employment rates fell gradually across cohorts entering from 1975 through 
. //around 2004, with the total decline amounting to around 2.5 percentage points. There was 
. //then an additional 1.8 percentage point decline between the 2004 and 2010 entrants, 
. //with stability thereafter.
. use `scratch'/extrapolate_coeffs.dta, clear

. keep if depvar=="empl"
(14,359 observations deleted)

. keep if ivartype=="FV" & fvname=="entrycohort"
(5,029 observations deleted)

. drop if fvval==2019
(32 observations deleted)

. reg b fvval if fvval>=1979 & fvval<=2004 & model=="mA1b"

      Source |       SS           df       MS      Number of obs   =        26
-------------+----------------------------------   F(1, 24)        =    232.64
       Model |  .001634074         1  .001634074   Prob > F        =    0.0000
    Residual |  .000168578        24  7.0241e-06   R-squared       =    0.9065
-------------+----------------------------------   Adj R-squared   =    0.9026
       Total |  .001802653        25  .000072106   Root MSE        =    .00265

------------------------------------------------------------------------------
           b | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       fvval |   -.001057   .0000693   -15.25   0.000    -.0012001    -.000914
       _cons |    2.09217   .1380164    15.16   0.000     1.807318    2.377022
------------------------------------------------------------------------------

. predict bhat1
(option xb assumed; fitted values)

. list fvval b bhat1 if model=="mA1b"

      +--------------------------------+
      | fvval           b        bhat1 |
      |--------------------------------|
1145. |  1970   -.0018583    .00981768 |
1146. |  1971    -.007865    .00876065 |
1147. |  1972   -.0042479    .00770362 |
1148. |  1973    -.002714    .00664659 |
1149. |  1974   -.0070079    .00558956 |
      |--------------------------------|
1150. |  1975   -.0084456    .00453253 |
1151. |  1976    -.008968    .00347549 |
1152. |  1977   -.0061911    .00241846 |
1153. |  1978   -.0025506    .00136143 |
1154. |  1979   -.0002561     .0003044 |
      |--------------------------------|
1155. |  1980   -.0032693   -.00075263 |
1156. |  1981   -.0013174   -.00180966 |
1157. |  1982   -.0038813   -.00286669 |
1158. |  1983   -.0010802   -.00392373 |
1159. |  1984           0   -.00498076 |
      |--------------------------------|
1160. |  1985   -.0047767   -.00603779 |
1161. |  1986   -.0075854   -.00709482 |
1162. |  1987   -.0047262   -.00815185 |
1163. |  1988   -.0066596   -.00920888 |
1164. |  1989   -.0082014   -.01026592 |
      |--------------------------------|
1165. |  1990   -.0126136   -.01132295 |
1166. |  1991   -.0162004   -.01237998 |
1167. |  1992    -.016717   -.01343701 |
1168. |  1993   -.0155205   -.01449404 |
1169. |  1994   -.0205043   -.01555107 |
      |--------------------------------|
1170. |  1995   -.0190286   -.01660811 |
1171. |  1996   -.0205837   -.01766514 |
1172. |  1997   -.0206799   -.01872217 |
1173. |  1998   -.0219299    -.0197792 |
1174. |  1999   -.0190565   -.02083623 |
      |--------------------------------|
1175. |  2000   -.0204288   -.02189326 |
1176. |  2001   -.0226121    -.0229503 |
1177. |  2002   -.0234313   -.02400733 |
1178. |  2003     -.02137   -.02506436 |
1179. |  2004   -.0231907   -.02612139 |
      |--------------------------------|
1180. |  2005   -.0288401   -.02717842 |
1181. |  2006   -.0272955   -.02823545 |
1182. |  2007   -.0296231   -.02929249 |
1183. |  2008   -.0331864   -.03034952 |
1184. |  2009   -.0374494   -.03140655 |
      |--------------------------------|
1185. |  2010   -.0407997   -.03246358 |
1186. |  2011   -.0379302   -.03352061 |
1187. |  2012   -.0382781   -.03457764 |
1188. |  2013   -.0379111   -.03563468 |
1189. |  2014   -.0393827   -.03669171 |
      |--------------------------------|
1190. |  2015   -.0377676   -.03774874 |
1191. |  2016   -.0338771   -.03880577 |
1192. |  2017   -.0405622    -.0398628 |
1193. |  2018   -.0441599   -.04091983 |
      +--------------------------------+

. 
. //It also shows a clear trend break in the 2000-2004 period.  A model with a linear trend 
. //and single trend break fits best when the break is in 2003, but breaks placed in any 
. //year from 2000 to 2006 all fit nearly as well.
. forvalues y=1995/2015 {
  2.   gen break=max(0,fvval-`y')
  3.   qui reg b fvval break if fvval>=1970 & model=="mB1b"
  4.   local fit=e(r2)
  5.   di "Break at `y': R2=`fit'"
  6.   drop break
  7. }
Break at 1995: R2=.8732147340122516
Break at 1996: R2=.889992658867649
Break at 1997: R2=.9061021606957416
Break at 1998: R2=.9208464583353223
Break at 1999: R2=.9342066896797651
Break at 2000: R2=.9443652361651512
Break at 2001: R2=.9516038895850549
Break at 2002: R2=.9570888665714674
Break at 2003: R2=.9605161644402033
Break at 2004: R2=.9596304860912048
Break at 2005: R2=.9545377643535767
Break at 2006: R2=.9476306238038423
Break at 2007: R2=.9364449653744594
Break at 2008: R2=.92056835620953
Break at 2009: R2=.9008505669626138
Break at 2010: R2=.8795634612810214
Break at 2011: R2=.8596713994576847
Break at 2012: R2=.8384577014558935
Break at 2013: R2=.816380717258738
Break at 2014: R2=.7928569158300984
Break at 2015: R2=.7706419271216673

. //Across the 18 cohorts since the 2000 entrants, cohort effects have fallen nearly five 
. //percentage points (relative to the 1984-2000 trend), with no sign that the downward 
. //trend stabilized after the Great Recession.
. list fvval b if model=="mB1b"

      +-------------------+
      | fvval           b |
      |-------------------|
1194. |  1970    .0102169 |
1195. |  1971    .0021712 |
1196. |  1972    .0039582 |
1197. |  1973    .0051082 |
1198. |  1974    .0003274 |
      |-------------------|
1199. |  1975   -.0022095 |
1200. |  1976   -.0037918 |
1201. |  1977   -.0019589 |
1202. |  1978    .0009334 |
1203. |  1979    .0022114 |
      |-------------------|
1204. |  1980   -.0021565 |
1205. |  1981   -.0012257 |
1206. |  1982   -.0046925 |
1207. |  1983   -.0017925 |
1208. |  1984           0 |
      |-------------------|
1209. |  1985   -.0029527 |
1210. |  1986    -.004052 |
1211. |  1987    .0001933 |
1212. |  1988   -.0006927 |
1213. |  1989   -.0008166 |
      |-------------------|
1214. |  1990   -.0039287 |
1215. |  1991   -.0051793 |
1216. |  1992   -.0034293 |
1217. |  1993   -.0001832 |
1218. |  1994   -.0031489 |
      |-------------------|
1219. |  1995    .0000163 |
1220. |  1996   -.0004038 |
1221. |  1997    .0004362 |
1222. |  1998   -.0004363 |
1223. |  1999    .0020977 |
      |-------------------|
1224. |  2000           0 |
1225. |  2001   -.0036457 |
1226. |  2002   -.0045182 |
1227. |  2003   -.0025299 |
1228. |  2004    -.004992 |
      |-------------------|
1229. |  2005   -.0107581 |
1230. |  2006   -.0094199 |
1231. |  2007   -.0116209 |
1232. |  2008   -.0155497 |
1233. |  2009   -.0208079 |
      |-------------------|
1234. |  2010   -.0259718 |
1235. |  2011   -.0252433 |
1236. |  2012   -.0280279 |
1237. |  2013   -.0300817 |
1238. |  2014    -.033997 |
      |-------------------|
1239. |  2015   -.0351187 |
1240. |  2016   -.0342747 |
1241. |  2017   -.0441342 |
1242. |  2018   -.0504605 |
      +-------------------+

. 
. 
. //Cohorts that enter the labor market when the state’s unemployment rate is elevated by 
. //1% have employment probabilities that are reduced by 0.7 percentage points at ages 22 
. //and 23, 0.5 percentage points at 24 and 25, and about 0.2 percentage points at 26 and 
. //27, after which the effect fades away. 
. use `scratch'/runatc_coeffs, clear

. list if ivartype=="Interaction" & model=="mD1b" & depvar=="empl"

       +-----------------------------------------------------------------------------------------------------------+
       | model   depvar      ivartype   cvname   fvname   fvval           b         se   normal~d          ivartxt |
       |-----------------------------------------------------------------------------------------------------------|
 4999. |  mD1b     empl   Interaction      ur0    expgp       0   -.0068793    .001416          .   c.ur0#c.expgp0 |
 5000. |  mD1b     empl   Interaction      ur0    expgp       2   -.0047073   .0008676          .   c.ur0#c.expgp2 |
 5001. |  mD1b     empl   Interaction      ur0    expgp       4   -.0019152   .0007724          .   c.ur0#c.expgp4 |
 5002. |  mD1b     empl   Interaction      ur0    expgp       6    .0006452   .0006465          .   c.ur0#c.expgp6 |
 5003. |  mD1b     empl   Interaction      ur0    expgp       8   -.0006241   .0006314          .   c.ur0#c.expgp8 |
       +-----------------------------------------------------------------------------------------------------------+

. list if cvname=="dur0" & model=="mD1b" & depvar=="empl"

       +---------------------------------------------------------------------------------------------------+
       | model   depvar     ivartype   cvname   fvname   fvval           b         se   normal~d   ivartxt |
       |---------------------------------------------------------------------------------------------------|
 5004. |  mD1b     empl   Continuous     dur0                .   -.0003098   .0006933          .      dur0 |
       +---------------------------------------------------------------------------------------------------+

. //A 1 percentage point higher unemployment rate in the year of entry reduces wages by 
. //about 1.1% at age 22-23, 1% at 24-25, 0.4% at 26-29, and 0.1% (not significant) at 30-31.
. list if ivartype=="Interaction" & model=="mD1b" & depvar=="rw_l"

       +-----------------------------------------------------------------------------------------------------------+
       | model   depvar      ivartype   cvname   fvname   fvval           b         se   normal~d          ivartxt |
       |-----------------------------------------------------------------------------------------------------------|
18794. |  mD1b     rw_l   Interaction      ur0    expgp       0   -.0111838   .0025654          .   c.ur0#c.expgp0 |
18795. |  mD1b     rw_l   Interaction      ur0    expgp       2   -.0102544   .0018107          .   c.ur0#c.expgp2 |
18796. |  mD1b     rw_l   Interaction      ur0    expgp       4   -.0037837   .0016609          .   c.ur0#c.expgp4 |
18797. |  mD1b     rw_l   Interaction      ur0    expgp       6   -.0040071   .0012652          .   c.ur0#c.expgp6 |
18798. |  mD1b     rw_l   Interaction      ur0    expgp       8   -.0014705   .0010421          .   c.ur0#c.expgp8 |
       +-----------------------------------------------------------------------------------------------------------+

. list if cvname=="dur0" & model=="mD1b" & depvar=="rw_l"

       +-------------------------------------------------------------------------------------------------+
       | model   depvar     ivartype   cvname   fvname   fvval         b         se   normal~d   ivartxt |
       |-------------------------------------------------------------------------------------------------|
18799. |  mD1b     rw_l   Continuous     dur0                .   .000467   .0020303          .      dur0 |
       +-------------------------------------------------------------------------------------------------+

. //Perhaps the most notable aspect of Figure 9 is that the sharp decline in cohort 
. //employment rates for the most recent cohorts is largely robust to the choice of 
. //controls....Across all four specifications, employment rates for the 2016 entrants 
. //are more than #5 percentage points below the 1990s trend.
. list if ivartype=="FV" & depvar=="empl" & fvname=="entrycohort" & ///
>         inlist(model, "mB1b", "mC1b", "mD1b", "mE1b") & fvval==2017

       +---------------------------------------------------------------------------------------------------------------+
       | model   depvar   ivartype   cvname        fvname   fvval           b         se   normal~d            ivartxt |
       |---------------------------------------------------------------------------------------------------------------|
 4612. |  mB1b     empl         FV            entrycohort    2017   -.0441342   .0105391          0   2017.entrycohort |
 4775. |  mC1b     empl         FV            entrycohort    2017   -.0503767   .0102863          0   2017.entrycohort |
 4944. |  mD1b     empl         FV            entrycohort    2017   -.0485031   .0104733          0   2017.entrycohort |
 5113. |  mE1b     empl         FV            entrycohort    2017   -.0498132   .0104032          0   2017.entrycohort |
       +---------------------------------------------------------------------------------------------------------------+

. //In the baseline decomposition we see a sharp drop in wages, about 2 percent, for the 
. //2009 entrants, with smaller reductions in 2007 and 2008. 
. list if ivartype=="FV" & depvar=="rw_l" & fvname=="entrycohort" & ///
>         inlist(model, "mB1b") & fvval>=2005 & fvval<=2017

       +---------------------------------------------------------------------------------------------------------------+
       | model   depvar   ivartype   cvname        fvname   fvval           b         se   normal~d            ivartxt |
       |---------------------------------------------------------------------------------------------------------------|
18395. |  mB1b     rw_l         FV            entrycohort    2005   -.0048658   .0060514          0   2005.entrycohort |
18396. |  mB1b     rw_l         FV            entrycohort    2006   -.0030537   .0084897          0   2006.entrycohort |
18397. |  mB1b     rw_l         FV            entrycohort    2007   -.0132364   .0075345          0   2007.entrycohort |
18398. |  mB1b     rw_l         FV            entrycohort    2008   -.0107641   .0083462          0   2008.entrycohort |
18399. |  mB1b     rw_l         FV            entrycohort    2009   -.0225183   .0085463          0   2009.entrycohort |
       |---------------------------------------------------------------------------------------------------------------|
18400. |  mB1b     rw_l         FV            entrycohort    2010    -.011861   .0088581          0   2010.entrycohort |
18401. |  mB1b     rw_l         FV            entrycohort    2011   -.0015734   .0118321          0   2011.entrycohort |
18402. |  mB1b     rw_l         FV            entrycohort    2012   -.0008702   .0131597          0   2012.entrycohort |
18403. |  mB1b     rw_l         FV            entrycohort    2013    .0018844   .0115671          0   2013.entrycohort |
18404. |  mB1b     rw_l         FV            entrycohort    2014    .0018572   .0108525          0   2014.entrycohort |
       |---------------------------------------------------------------------------------------------------------------|
18405. |  mB1b     rw_l         FV            entrycohort    2015    .0164088   .0135308          0   2015.entrycohort |
18406. |  mB1b     rw_l         FV            entrycohort    2016    .0131114   .0133867          0   2016.entrycohort |
18407. |  mB1b     rw_l         FV            entrycohort    2017    .0396942   .0217559          0   2017.entrycohort |
       +---------------------------------------------------------------------------------------------------------------+

. //The 2011 and subsequent cohorts have wages about 2% higher, on average, than earlier
. // cohorts, after adjusting for normal early career scarring effects.        
. list if ivartype=="FV" & depvar=="rw_l" & fvname=="entrycohort" & ///
>         inlist(model, "mB1b", "mC1b", "mD1b", "mE1b") & fvval>=2011 & fvval<=2017

       +---------------------------------------------------------------------------------------------------------------+
       | model   depvar   ivartype   cvname        fvname   fvval           b         se   normal~d            ivartxt |
       |---------------------------------------------------------------------------------------------------------------|
18401. |  mB1b     rw_l         FV            entrycohort    2011   -.0015734   .0118321          0   2011.entrycohort |
18402. |  mB1b     rw_l         FV            entrycohort    2012   -.0008702   .0131597          0   2012.entrycohort |
18403. |  mB1b     rw_l         FV            entrycohort    2013    .0018844   .0115671          0   2013.entrycohort |
18404. |  mB1b     rw_l         FV            entrycohort    2014    .0018572   .0108525          0   2014.entrycohort |
18405. |  mB1b     rw_l         FV            entrycohort    2015    .0164088   .0135308          0   2015.entrycohort |
       |---------------------------------------------------------------------------------------------------------------|
18406. |  mB1b     rw_l         FV            entrycohort    2016    .0131114   .0133867          0   2016.entrycohort |
18407. |  mB1b     rw_l         FV            entrycohort    2017    .0396942   .0217559          0   2017.entrycohort |
18564. |  mC1b     rw_l         FV            entrycohort    2011    .0035334   .0118422          0   2011.entrycohort |
18565. |  mC1b     rw_l         FV            entrycohort    2012    .0022955   .0131858          0   2012.entrycohort |
18566. |  mC1b     rw_l         FV            entrycohort    2013    .0027014   .0116364          0   2013.entrycohort |
       |---------------------------------------------------------------------------------------------------------------|
18567. |  mC1b     rw_l         FV            entrycohort    2014   -.0002308   .0109895          0   2014.entrycohort |
18568. |  mC1b     rw_l         FV            entrycohort    2015     .012908   .0131932          0   2015.entrycohort |
18569. |  mC1b     rw_l         FV            entrycohort    2016    .0077966   .0132783          0   2016.entrycohort |
18570. |  mC1b     rw_l         FV            entrycohort    2017    .0318206   .0220973          0   2017.entrycohort |
18733. |  mD1b     rw_l         FV            entrycohort    2011    .0242679   .0137102          0   2011.entrycohort |
       |---------------------------------------------------------------------------------------------------------------|
18734. |  mD1b     rw_l         FV            entrycohort    2012    .0225901   .0146957          0   2012.entrycohort |
18735. |  mD1b     rw_l         FV            entrycohort    2013    .0221275    .012831          0   2013.entrycohort |
18736. |  mD1b     rw_l         FV            entrycohort    2014    .0145597   .0122785          0   2014.entrycohort |
18737. |  mD1b     rw_l         FV            entrycohort    2015    .0216302   .0138893          0   2015.entrycohort |
18738. |  mD1b     rw_l         FV            entrycohort    2016    .0126403   .0133849          0   2016.entrycohort |
       |---------------------------------------------------------------------------------------------------------------|
18739. |  mD1b     rw_l         FV            entrycohort    2017     .034552   .0223355          0   2017.entrycohort |
18902. |  mE1b     rw_l         FV            entrycohort    2011    .0222781   .0138899          0   2011.entrycohort |
18903. |  mE1b     rw_l         FV            entrycohort    2012    .0201258   .0148639          0   2012.entrycohort |
18904. |  mE1b     rw_l         FV            entrycohort    2013    .0195527    .013086          0   2013.entrycohort |
18905. |  mE1b     rw_l         FV            entrycohort    2014    .0119448   .0124587          0   2014.entrycohort |
       |---------------------------------------------------------------------------------------------------------------|
18906. |  mE1b     rw_l         FV            entrycohort    2015    .0207249   .0135343          0   2015.entrycohort |
18907. |  mE1b     rw_l         FV            entrycohort    2016    .0143532   .0136735          0   2016.entrycohort |
18908. |  mE1b     rw_l         FV            entrycohort    2017    .0370075   .0226727          0   2017.entrycohort |
       +---------------------------------------------------------------------------------------------------------------+

. 
. 
. use `scratch'/cohfxregs, clear

. list model entrycohort b fitted_preGR_1 resid_preGR_1 if depvar=="empl" & model=="mB1b" ///
>           & entrycohort>=2007 & entrycohort<.

       +--------------------------------------------------------+
       | model   entryc~t           b   fitted_p~1   resid_pr~1 |
       |--------------------------------------------------------|
 5211. |  mB1b       2007   -1.162088   -.26986906   -.89221923 |
 5212. |  mB1b       2008   -1.554973   -.48176355   -1.0732095 |
 5213. |  mB1b       2009   -2.080788   -1.0576064   -1.0231815 |
 5214. |  mB1b       2010   -2.597179   -1.1270863   -1.4700922 |
 5215. |  mB1b       2011   -2.524334   -1.0383278    -1.486006 |
       |--------------------------------------------------------|
 5216. |  mB1b       2012   -2.802794   -.93374543    -1.869049 |
 5217. |  mB1b       2013   -3.008173   -.84498691   -2.1631859 |
 5218. |  mB1b       2014   -3.399704   -.67710918   -2.7225952 |
 5219. |  mB1b       2015   -3.511866   -.55670297   -2.9551632 |
 5220. |  mB1b       2016   -3.427475   -.51541598    -2.912059 |
       |--------------------------------------------------------|
 5221. |  mB1b       2017   -4.413418   -.45830514   -3.9551133 |
 5222. |  mB1b       2018   -5.046054   -.40119431   -4.6448593 |
 5223. |  mB1b       2019   -4.698621     -.391555   -4.3070665 |
       +--------------------------------------------------------+

. list model entrycohort b fitted_preGR_1 resid_preGR_1 if depvar=="empl" & model=="mE1b" ///
>           & entrycohort>=2007 & entrycohort<.

       +--------------------------------------------------------+
       | model   entryc~t           b   fitted_p~1   resid_pr~1 |
       |--------------------------------------------------------|
 6415. |  mE1b       2007   -.6949209   -.01453028   -.68039064 |
 6416. |  mE1b       2008   -.9786439   -.08825717   -.89038675 |
 6417. |  mE1b       2009   -1.386035   -.31363376   -1.0724009 |
 6418. |  mE1b       2010   -1.982296   -.32801946    -1.654277 |
 6419. |  mE1b       2011    -1.99011    -.2764705   -1.7136399 |
       |--------------------------------------------------------|
 6420. |  mE1b       2012   -2.358746   -.21832807   -2.1404181 |
 6421. |  mE1b       2013   -2.652482   -.16677911    -2.485703 |
 6422. |  mE1b       2014   -3.253532   -.08226282   -3.1712694 |
 6423. |  mE1b       2015   -3.585634   -.01752693   -3.5681067 |
 6424. |  mE1b       2016   -3.757523    .01424164    -3.771765 |
       |--------------------------------------------------------|
 6425. |  mE1b       2017   -4.981317    .05260367   -5.0339203 |
 6426. |  mE1b       2018   -5.864071     .0909657   -5.9550372 |
 6427. |  mE1b       2019    -5.63083    .10954733   -5.7403769 |
       +--------------------------------------------------------+

. 
. if `doasproject'==1 {
.   project, creates(`scratch'/`dofile'_agegprw_l.dta)
project GRscar_erratum > do-file creates: "/scratch/public/jr_ra/GRscarring2024/erratum/scratch/miscstats_agegprw_l.dta" filesig(238
> 6825813:26305)
.   project, creates(`scratch'/`dofile'_agegpempl.dta)
project GRscar_erratum > do-file creates: "/scratch/public/jr_ra/GRscarring2024/erratum/scratch/miscstats_agegpempl.dta" filesig(125
> 7441953:52744)
. }

. 
end of do-file
      name:  plog_921
       log:  /accounts/projects/jr_ra/GRscarring/erratum/programs/analysis/miscstats.log
  log type:  text
 closed on:  27 Nov 2024, 18:08:18
------------------------------------------------------------------------------------------------------------------------------------
